ArticleBMC genomics2022
Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Decoding the Role of Lipid Metabolism and Membrane Dynamics in Melanoma.International journal of molecular sciences · 2026Review
- Emerging Roles of Ferroptosis in Skin Pathophysiology.The Journal of investigative dermatology · 2025Review
- Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification.Frontiers in immunology · 2025Article
- Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning.Apoptosis : an international journal on programmed cell death · 2024Article
- Development of a biomarker signature associated with anoikis to predict prognosis and immunotherapy response in melanoma.Archives of dermatological research · 2024Article
- Development of a Macrophage-Related Risk Model for Metastatic Melanoma.International journal of molecular sciences · 2023Article
- Characterization of coagulation-related gene signature to predict prognosis and tumor immune microenvironment in skin cutaneous melanoma.Frontiers in oncology · 2022Article
- A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma.Frontiers in genetics · 2022Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMelanoma is a highly heterogeneous and aggressive cutaneous malignancy. Ferroptosis, a new pathway of cell death depending on the intracellar iron, has been shown to be significantly associated with apoptosis of a number of tumors, including melanoma. Nevertheless, the relationship between ferroptosis-related genes (FRGs) and the melanoma patients' prognosis needs to be explored.
methodsDownload expression profiles of FRGs and clinical data from The Cancer Genome Atlas (TCGA) database. 70% data were randomly selected from the TCGA database and utilized the univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model to create a prognostic model, and the remaining 30% was used to validate the predictive power of the model. In addition, GSE65904 and GSE22153 date sets as the verification cohort to testify the predictive ability of the signature.
resultsWe identified nine FRGs relating with melanoma patients' overall survival (OS) and established a prognostic model based on their expression. During the research, patients were divided into group of high-risk and low-risk according to the results of LASSO regression analysis. Survival time was significantly longer in the low-risk group than that of in the high-risk group (P < 0.001). Enrichment analysis of different risk groups demonstrated that the reasons for the difference were related to immune-related pathways, and the degree of immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group.
conclusionsThe FRG prognostic model we established can predict the prognosis of melanoma patients and may further guide subsequent treatment.
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